code: def forward(
      self,
      utterance: torch.Tensor,
      right_context: torch.Tensor,
      state: Optional[torch.Tensor],
  ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
      input = torch.cat((right_context, utterance))  # input: (T, B, D)
      x = self.pre_conv(input)
      x_right_context, x_utterance = (
          x[: right_context.size(0), :, :],
          x[right_context.size(0) :, :, :],
      )
      x_utterance = x_utterance.permute(1, 2, 0)  # (B, D, T_utterance)
  
      if state is None:
          state = torch.zeros(
              input.size(1),
              input.size(2),
              self.state_size,
              device=input.device,
              dtype=input.dtype,
          )  # (B, D, T)
      state_x_utterance = torch.cat([state, x_utterance], dim=2)
  
      conv_utterance = self.conv(
          state_x_utterance
      )  # (B, D, T_utterance)
      conv_utterance = conv_utterance.permute(2, 0, 1)
  
      if self.right_context_length > 0:
          # (B * num_segments, D, right_context_length + kernel_size - 1)
          right_context_block = self._split_right_context(
              state_x_utterance.permute(2, 0, 1), x_right_context
          )
          conv_right_context_block = self.conv(
              right_context_block
          )  # (B * num_segments, D, right_context_length)
          # (T_right_context, B, D)
          conv_right_context = self._merge_right_context(
              conv_right_context_block, input.size(1)
          )
          y = torch.cat([conv_right_context, conv_utterance], dim=0)
      else:
          y = conv_utterance
  
      output = self.post_conv(y) + input
      new_state = state_x_utterance[:, :, -self.state_size :]
      return (
          output[right_context.size(0) :],
          output[: right_context.size(0)],
          new_state,
      )
  
imports: import math
  from typing import List, Optional, Tuple
  import torch
  from torchaudio.models.emformer import _EmformerAttention, _EmformerImpl, _get_weight_init_gains
label_desc: Use image node_id 4 for calling the _ConvolutionModule obj's underlying member method code with example usage: obj.forward(utterance, right_context, state) and returns: output,  output,  new_state